Anxious learners work harder to achieve the same result.

Cognitive load and Attentional Control Theory.

Is there a link between emotions and cognitive load? This was a question Sweller pondered in his 2019 paper on 20 years of Cognitive Load Theory. He didn’t reach any concrete conclusions, but noted a few relevant studies. We know anxiety can impact memory consolidation, but the relationship is complex (there appears to be a difference between consolidation and recall, for example). Heightened psychological arousal can certainly help with learning, but too much will impede it (the so-called Yerkes-Dodson law).

There’s a tendency within the teaching and learning professions to only view cognitive load from the perspective of Cognitive Load Theory - wholly understandable, seeing as CLT provides a useful framework from which to design more effective learning environments by taking evidence from cognitive psychology into account. But rarely does CLT concern itself with emotions, which often appear to be beyond the remit of the framework.

Emotions do impact load, however, and we can’t always rely on a single model to explain all of its facets. Attentional Control Theory (ACT) developed by Michael Eysenck at Royal Holloway, University of London, proposes that anxiety acts as a distracter that depletes the cognitive capacity needed for solving complex problems (Eysenck et al., 2007). Or, anxiety raises load beyond manageable or useful levels.

ACT emphasises the impact of anxiety on executive function (e.g. working memory, emotion regulation, and cognitive flexibility), leading to an increased focus on threat-related stimuli. While the body is in a heightened state of vigilance (fight or flight), higher mental functions take a back a seat, impairing attentional processes and working memory. This is well known and supported by a plethora of evidence.

Attention and working memory are closely entwined and we are often compelled to select what we pay attention to from a multitude of inputs. Cowan describes working memory as the portion of long-term memory that is currently the focus of attention. In this respect, working memory is activated long-term memory. But other stimuli distract us and add to the mental load. Distracters can be external and internal, including worrying thoughts and negative self-evaluation. Our brains, therefore, are constantly juggling demands for our attention.

Attention is, therefore, the primary driver of cognition. Within the premise of ACT, attention can be goal directed (or top-down) and distraction orientated (bottom-up). The Frontoparietal Network (FPN), a network of brain regions in the frontal and parietal lobes of the brain, is thought to be responsible for top-down attentional control. It’s where we direct our attention in pursuit of a specific goal. When we’re given a task to complete, it ensures that we focus on each step and ignore distractions. The FPN is like a conductor in an orchestra, making sure that all the instruments play in harmony and produce a cohesive symphony. It, therefore, plays a vital role in supporting executive functions through interaction with working memory, planning and decision-making, and inhibitory control.

Distractive (bottom-up) processing is associated with the Default Mode Network (DMN). The DMN is involved in self-referential thinking, mind-wandering, and internally directed thoughts. These can interfere with attentional control mechanisms. Unsurprisingly, the DMN is more active in people with high trait anxiety. While the FPN attempts to keep us focussed and on track, the DMN tries its best to distract us.

A third network known as the Cingular Opercula Network (CON) attempts to resolve conflicts that arise between our goals and our distractions, acting as a kind of arbitrator between the FPN and the DMN. Because the DMN dominates in more anxious people, the CON has to work much harder to resolve any conflict by redirecting attention back to the goal. While this may help maintain performance effectiveness, it comes at the cost of mental effort and reduces processing efficiency.

Processing effectiveness is the quality of performance on a task. If I were to give you a series of mathematical equations to solve, your processing effectiveness could be calculated by the number of equations you solved accurately. Processing efficiency is defined as the relationship between the effectiveness of your performance (the number of correct answers) and the effort or resources expended to achieve that level of performance. A decrease in efficiency means that you’re going to be employing more resources just to maintain the same level of performance. Anxious learners will have to work much harder to achieve the same result as their less anxious peers.

Take, as an example, maths anxiety. This may initially arise through several factors, including poor working memory function or negative early experiences. From then on, it’s often self-sustaining. A child in the early stages of formal education, for example, may struggle because of limited working memory capacity. This could be exacerbated because of factors, including those from within the teaching environment. Young children rapidly develop a sense of how they perceive themselves within academic settings (academic self-concept), so a child who struggles early will develop the belief that they aren’t good at maths. This, then, increases levels of anxiety around the subject, impacting factors such as motivation and self-efficacy. Consequently, raised levels of anxiety lead to increasingly poor performance and the cycle repeats.

From a cognitive load theory perspective, emotions represent extraneous cognitive load (factors unrelated to the learning task that require processing nevertheless). These factors are going to compete for resources - it doesn’t make any difference if they are memory limitations, distractions, or emotions. But emotions may also affect intrinsic cognitive load, although the mechanisms by which they do this are less well understood. It most likely relates to emotional regulation, the ability to keep emotions in check. This inevitably involves elements of academic buoyancy.

According to Jan Plass of New York University and Slava Kalyuga from the University of New South Wales, emotions are also likely to influence motivation. Motivation affects mental effort, so it’s going to impact the extent to which we integrate new information with currently stored schemas. Plass and Kalyuga also suggest that emotions affect memory by both broadening and narrowing cognitive resource (the so-called broaden and build framework). However, if levels of anxiety are too high, this limits motivation and cognitive engagement, and there is a greater chance of abandoning the task. When we are under positive affect, information in long-term memory is more readily available, while negative emotions increase extraneous load (Plass & Kalyuga, 2019).

Is it realistic, then, to propose that strategies purported to manage load also reduce unhelpful levels of anxiety? This is certainly what cognitive load theory would imply. Take, for example, a student displaying behaviours consistent with maths anxiety. If study materials are over-cluttered, include complex language, and are generally designed in such a way as to place unwarranted stress on limited cognitive resources, it would be likely that the student would experience higher levels of anxiety. Our student is under attack from all sides: the complexity and poorly designed materials and fear and worry brought about by their inherent anxiety over maths.

This means that strategies designed to manage load might also reduce excess anxiety. Once again, emotions are impacting learning and we can potentially employ strategies used to make learning more efficient to help learners manage emotions.